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Abstract Kidney fibrosis causes irreversible structural damage in chronic kidney disease and is characterized by aberrant extracellular matrix (ECM) accumulation. Although glutamyl-prolyl-tRNA synthetase 1 (EPRS1) is a crucial enzyme involved in proline-rich protein synthesis, its role in kidney fibrosis remains unclear. The present study revealed that EPRS1 expression levels were increased in the fibrotic kidneys of patients and mice, especially in fibroblasts and proximal tubular epithelial cells, on the basis of single-cell analysis and immunostaining of fibrotic kidneys. Moreover, C57BL/6 EPRS1tm1b heterozygous knockout (Eprs1 +/−) and pharmacological EPRS1 inhibition with the first-in-class EPRS1 inhibitor DWN12088 protected against kidney fibrosis and dysfunction by preventing fibroblast activation and proximal tubular injury. Interestingly, in vitro assays demonstrated that EPRS1-mediated nontranslational pathways in addition to translational pathways under transforming growth factor β-treated conditions by phosphorylating SMAD family member 3 in fibroblasts and signal transducers and activators of transcription 3 in injured proximal tubules. EPRS1 knockdown and catalytic inhibition suppressed these pathways, preventing fibroblast activation, proliferation, and subsequent collagen production. Additionally, we revealed that EPRS1 caused mitochondrial damage in proximal tubules but that this damage was attenuated by EPRS1 inhibition. Our findings suggest that the EPRS1-mediated ECM accumulation induces kidney fibrosis via fibroblast activation and mitochondrial dysfunction. Therefore, targeting EPRS1 could be a potential therapeutic target for alleviating fibrotic injury in chronic kidney disease.

Abstract Background In the wake of the coronavirus disease-19 (COVID-19) pandemic, research on the difficulties faced by epidemiologists conducting epidemiological investigations has been progressing steadily. However, few studies have developed applications (apps) that can directly support epidemiological investigations via information and communication (ICT) technology, and conducted usability evaluations on them via user responses. This has caused difficulties when developing such technologies. We introduced and evaluated two mobile apps that support epidemiological investigations. This study attempted to identify the predictors affecting the acceptance of infectious disease self-management apps. Methods We developed two infectious disease self-management smartphone apps for epidemiological investigations: KODARI (the Korean version) and MEDARI (in English version). We collected data from 248 users of KODARI by surveying Korean citizens. This study was conducted from November 15 to December 14, 2022. We used multiple regression analysis to identify the variables that affected continuous intention to use the KODARI app. We conducted two independent-samples t-tests to determine whether there were any differences in the perception of each variable in relation to demographic and COVID-19-related user characteristics. Results The factors that affected continuous intention to use the KODARI app, in order of relative importance, were: price value, satisfaction, performance expectancy, and facilitating conditions. Overall, male participants were more satisfied with the KODARI app than female ones, and more willing to continue using it. The male participants also evaluated facilitating conditions more positively than the female ones did. Married participants rated the app higher than single ones in terms of price value, performance expectancy, and continued intention to use. Conclusions The study suggests factors that increase the use of health apps and suggests that use of these apps may increase further in the event of a future pandemics. These results are expected to help researchers study other infectious disease apps in the context of public health surveillance.

Abstract Single-cell technologies have enhanced comprehensive knowledge regarding the human brain by facilitating an extensive transcriptomic census across diverse brain regions. Nevertheless, understanding the cellular and temporal specificity of neurological disorders remains ambiguous due to developmental variations. To address this gap, we illustrated the dynamics of disorder risk gene expression under development by integrating multiple single-cell RNA sequencing datasets. We constructed a comprehensive single-cell atlas of the developing human brain, encompassing 393,060 single cells across diverse developmental stages. Temporal analysis revealed the distinct expression patterns of disorder risk genes, including those associated with autism, highlighting their temporal regulation in different neuronal and glial lineages. We identified distinct neuronal lineages that diverged across developmental stages, each exhibiting temporal-specific expression patterns of disorder-related genes. Lineages of nonneuronal cells determined by molecular profiles also showed temporal-specific expression, indicating a link between cellular maturation and the risk of disorder. Furthermore, we explored the regulatory mechanisms involved in early brain development, revealing enriched patterns of fetal cell types associated with neuronal disorders indicative of the prenatal stage’s influence on disease determination. Our findings facilitate unbiased comparisons of cell type‒disorder associations and provide insight into dynamic alterations in risk genes during development, paving the way for a deeper understanding of neurological disorders.

IntroductionFine dust exposure has been reported to affect patients with prostate cancer, making it crucial to understand how environmental pollutants impact health. This study aimed to determine the risk of prostate cancer in South Korea associated with moderate levels of fine dust (PM10) exposure.MethodsWe analyzed data from 20,430 individuals in the National Health Insurance Sharing Service database from 2010 to 2020, comparing a new prostate cancer group (n = 4,071, 19.9%) with a non-prostate cancer group (n = 16,359, 80.1%). Using PM10 data from Air Korea's annual average air quality database, we conducted logistic regression analysis to assess the risk of prostate cancer.ResultsOur findings indicate that even moderate PM10 exposure is a risk factor for developing prostate cancer. Additionally, even at low levels of PM2.5, moderate PM10 exposure significantly impacts prostate cancer development, with lifestyle ha bits potentially lowering this risk.DiscussionThese results underscore the need for stricter environmental standards for PM10 and proactive policies to reduce public health and long-term social costs. Public awareness, including mask use and air quality management, is essential.

Erectile dysfunction (ED) affects a significant proportion of men aged 40–70 and is caused by cavernous tissue dysfunction. Presently, the most common treatment for ED is phosphodiesterase 5 inhibitors; however, this is less effective in patients with severe vascular disease such as diabetic ED. Therefore, there is a need for development of new treatment, which requires a better understanding of the cavernous microenvironment and cell-cell communications under diabetic condition. Pericytes are vital in penile erection; however, their dysfunction due to diabetes remains unclear. In this study, we performed single-cell RNA sequencing to understand the cellular landscape of cavernous tissues and cell type-specific transcriptional changes in diabetic ED. We found a decreased expression of genes associated with collagen or extracellular matrix organization and angiogenesis in diabetic fibroblasts, chondrocytes, myofibroblasts, valve-related lymphatic endothelial cells, and pericytes. Moreover, the newly identified pericyte-specific marker, Limb Bud-Heart (Lbh), in mouse and human cavernous tissues, clearly distinguishing pericytes from smooth muscle cells. Cell-cell interaction analysis revealed that pericytes are involved in angiogenesis, adhesion, and migration by communicating with other cell types in the corpus cavernosum; however, these interactions were highly reduced under diabetic conditions. Lbh expression is low in diabetic pericytes, and overexpression of LBH prevents erectile function by regulating neurovascular regeneration. Furthermore, the LBH-interacting proteins (Crystallin Alpha B and Vimentin) were identified in mouse cavernous pericytes through LC-MS/MS analysis, indicating that their interactions were critical for maintaining pericyte function. Thus, our study reveals novel targets and insights into the pathogenesis of ED in patients with diabetes.

Abstract Although KRASG12C inhibitors have shown promising activity in lung adenocarcinomas harbouring KRASG12C, acquired resistance to these therapies eventually occurs in most patients. Re-expression of KRAS is thought to be one of the main causes of acquired resistance. However, the mechanism through which cancer cells re-express KRAS is not fully understood. Here, we report that the Hedgehog signal is induced by KRASG12C inhibitors and mediates KRAS re-expression in cancer cells treated with a KRASG12C inhibitor. Further, KRASG12C inhibitors induced the formation of primary cilia and activated the Hedgehog-GLI-1 pathway. GLI-1 binds to the KRAS promoter region, enhancing KRAS promoter activity and KRAS expression. Inhibition of GLI using siRNA or the smoothened (Smo) inhibitor suppressed re-expression of KRAS in cells treated with a KRASG12C inhibitor. In addition, we demonstrate that KRASG12C inhibitors decreased Aurora kinase A (AURKA) levels in cancer cells, and inhibition of AURKA using siRNA or inhibitors led to increased expression levels of GLI-1 and KRAS even in the absence of KRAS inhibitor. Ectopic expression of AURKA attenuated the effect of KRASG12C inhibitors on the expression of GLI-1 and re-expression of KRAS. Together, these findings demonstrate the important role of AURKA, primary cilia, and Hedgehog signals in the re-expression of KRAS and therefore the induction of acquired resistance to KRASG12C inhibitors, and provide a rationale for targeting Hedgehog signalling to overcome acquired resistance to KRASG12C inhibitors.

Objectives Distributed research networks (DRNs) facilitate multicenter research by enabling the use of multicenter data; therefore, they are increasingly utilized in healthcare fields. Despite the numerous advantages of DRNs, it is crucial to understand researchers’ acceptance of these networks to ensure their effective application in multicenter research. In this study, we sought to identify the factors influencing the adoption of DRNs among researchers in Korea. Methods We used snowball sampling to collect data from 149 researchers between July 7 and August 28, 2020. Five factors were used to formulate the hypotheses and research model: data accessibility, usefulness, ease of use, data security risk, and intention to use DRNs. We applied a structural equation model to identify relationships within the research model. Results Data accessibility and data security were critical to the acceptance and use of DRNs. The usefulness of DRNs partially mediated the relationship between data accessibility and the intention to use DRNs. Interestingly, ease of use did not influence the intention to use DRNs, but it was affected by data accessibility. Furthermore, ease of use impacted the perceived usefulness of DRNs. Conclusions This study highlighted major factors that can promote the broader adoption and utilization of DRNs. Consequently, these findings can contribute to the expansion of active multicenter research using DRNs in the field of healthcare research.

Abstract γδ T cells are evolutionarily conserved T lymphocytes that manifest unique antitumor efficacy independent of tumor mutation burden (TMB) and conventional human leukocyte antigen (HLA) recognition. However, the dynamic changes in their T cell receptor (TCR) repertoire during cancer progression and treatment courses remain unclear. Here, a comprehensive characterization of γδTCR repertoires are performed in thyroid cancers with divergent differentiation states through cross‐sectional studies. The findings revealed a significant correlation between the differentiation states and TCR repertoire diversity. Notably, highly expanded clones are prominently enriched in γδ T cell compartment of dedifferentiated patients. Moreover, by longitudinal investigations of the γδ T cell response to various antitumor therapies, it is found that the emergence and expansion of the Vδ2neg subset may be potentially associated with favorable clinical outcomes after post‐radiotherapeutic immunotherapy. These findings are further validated at single‐cell resolution in both advanced thyroid cancer patients and a murine model, underlining the importance of further investigations into the role of γδTCR in cancer immunity and therapeutic strategies.

IntroductionNumerous COVID-19-related apps were widely used during the COVID-19 pandemic. Among them, those supporting epidemiological investigations were particularly useful. This study explored the effectiveness of apps that support epidemiological investigations, factors influencing users’ intention to use them, and ways to encourage their use.MethodsWe developed and evaluated the KODARI app to demonstrate its importance in epidemiological investigations. After adapting a questionnaire based on an existing evaluation framework for COVID-19–related apps, we collected data from 276 participants through an online survey conducted between April 28 and May 25, 2023. We conducted two independent sample t-tests to determine the differences between each variable according to demographic characteristics and a multiple regression analysis to identify factors affecting intention to use.ResultsUsers were generally satisfied with the KODARI. We observed differences in sex, age, marital status, occupational characteristics, and experience with epidemiological investigation. Females rated the app’s information accuracy higher than males. Males had a higher intention to use than females. Participants aged under 35 years rated information accuracy and transparency highly, whereas single participants rated information accuracy higher than married participants. Occupational groups with frequent interactions with others evaluated their self-determination regarding the application. The app’s self-determination was highly valued among participants with experience in epidemiological investigations. By investigating the factors affecting the intention to use the app, we confirmed that effectiveness, self-determination, and usability significantly affected the intention to use.DiscussionThis study demonstrated the effectiveness of app supporting epidemiological investigations, identified meaningful factors that influence intention to use, and confirmed the applicability of our new framework by considering the specificity of infectious disease situations such as COVID-19. This study provides a new basis for future epidemiological studies.

Abstract Background Plants memorize previous pathogen attacks and are “primed” to produce a faster and stronger defense response, which is critical for defense against pathogens. In plants, cytosines in transposons and gene bodies are reported to be frequently methylated. Demethylation of transposons can affect disease resistance by regulating the transcription of nearby genes during defense response, but the role of gene body methylation (GBM) in defense responses remains unclear. Results Here, we find that loss of the chromatin remodeler decrease in DNA methylation 1 (ddm1) synergistically enhances resistance to a biotrophic pathogen under mild chemical priming. DDM1 mediates gene body methylation at a subset of stress-responsive genes with distinct chromatin properties from conventional gene body methylated genes. Decreased gene body methylation in loss of ddm1 mutant is associated with hyperactivation of these gene body methylated genes. Knockout of glyoxysomal protein kinase 1 (gpk1), a hypomethylated gene in ddm1 loss-of-function mutant, impairs priming of defense response to pathogen infection in Arabidopsis. We also find that DDM1-mediated gene body methylation is prone to epigenetic variation among natural Arabidopsis populations, and GPK1 expression is hyperactivated in natural variants with demethylated GPK1. Conclusions Based on our collective results, we propose that DDM1-mediated GBM provides a possible regulatory axis for plants to modulate the inducibility of the immune response.

The genus Tuponia Reuter, 1875 belongs to the subfamily Phylinae and comprises 91 species worldwide. Before this study, only T. koreana Kim & Jung had been recorded from the Korean Peninsula.Two species of Tuponia Reuter, 1910 are recognised from the Korean Peninsula including the first record of T. mongolica Drapolyuk, 1980. T. koreana Kim & Jung, 2021 is proposed as a junior synonym of T. chinensis Zheng & Li, 1992. The species is identified, based on the dorsal habitus and male and female genitalic structures. A brief discussion of the distribution of Korean Tuponia species also is presented.

Abstract The prevalence of genital human papillomavirus (HPV) in women with endometriosis has never been reported in a national representative survey. We aimed to investigate the association of endometriosis with the prevalence of HPV. We analyzed the data on 1768 women (representing 43,824,157 women) in the United States aged 20–54 years from the National Health and Nutrition Examination Survey in the prevaccination era (2003–2006). The diagnosis of endometriosis was based on a self-report. The prevalence of any HPV in women with endometriosis did not differ from that in women without endometriosis after controlling for potential confounders such as age, ethnicity, family income, marital status, and the number of deliveries (adjusted prevalence ratio (aPR) 0.84, 95% confidence interval (CI) 0.61–1.15). No significant association was found between the prevalence of high-risk HPV and the diagnosis of endometriosis (aPR 0.71, 95% CI 0.44–1.14). If the participants were not covered by health insurance, the prevalence of any HPV infection in women with endometriosis was higher than in those without endometriosis (aPR 1.44, 95% CI 0.94–2.20). In contrast, in a subgroup who had health insurance, a lower prevalence of any HPV infection was observed in women with endometriosis (aPR 0.71, 95% CI 0.50–1.03), and P for interaction was statistically significant (P = 0.01). There was no association between endometriosis and HPV infection in this study of HPV vaccine-naïve women of reproductive age. The association was not different by the type of HPV. However, access to healthcare may modify the association between endometriosis and HPV infection.

The contribution of transposable elements and endogenous retroviruses to renal fibroinflammation is currently unknown. Here, the authors comprehensively profile the expression of transposable elements and endogenous retroviruses (ERVs) in kidneys of patients and mouse disease models and find expression of ERVs in diseased kidneys activate cytosolic nucleotide sensors contributing to cytokine release and renal fibroinflammation.

Background & Aims: Histamine in the stomach traditionally is considered to regulate acid secretion but also has been reported to participate in macrophage differentiation, which plays an important role in tissue homeostasis. Therefore, this study aimed to uncover the precise role of histamine in mediating macrophage differentiation and in maintaining stomach homeostasis. Methods: Here, we expand on this role using histidine decarboxylase knockout (Hdc-/-) mice with hypertrophic gastropathy. In-depth in vivo studies were performed in Hdc-/- mice, germ-free Hdc-/- mice, and bone-marrow–transplanted Hdc-/- mice. The stomach macrophage populations and function were characterized by flow cytometry. To identify stomach macrophages and find the new macrophage population, we performed single-cell RNA sequencing analysis on Hdc+/+ and Hdc-/- stomach tissues. Results: Single-cell RNA sequencing and flow cytometry of the stomach cells of Hdc-/- mice showed alterations in the ratios of 3 distinct tissue macrophage populations (F4/80+Il1bhigh, F4/80+CD93+, and F4/80-MHC class IIhighCD74high). Tissue macrophages of the stomachs of Hdc-/- mice showed impaired phagocytic activity, increasing the bacterial burden of the stomach and attenuating hypertrophic gastropathy in germ-free Hdc-/- mice. The transplantation of bone marrow cells of Hdc+/+ mice to Hdc-/- mice recovered the normal differentiation of stomach macrophages and relieved the hypertrophic gastropathy of Hdc-/- mice. Conclusions: This study showed the importance of histamine signaling in tissue macrophage differentiation and maintenance of gastric homeostasis through the suppression of bacterial overgrowth in the stomach.

Background The entanglement of multiple central venous catheters is a rare and serious complication. The Swan-Ganz catheter is a responsible for various cases. Case A 66-year-old male patient was under general anesthesia for a coronary artery bypass graft surgery. As he had a pre-existing Perm catheter in the right subclavian vein, a Swan-Ganz catheter was inserted into the left internal jugular vein. Chest radiograph after catheter placement revealed that the Perm catheter had migrated to the left brachiocephalic vein. The surgeon attempted to reposition it manually, but postoperative radiograph showed that it had rolled into a loop. On postoperative day 1, radiological intervention was performed to untangle the loop, which was successful. Conclusions After placing a Swan-Ganz catheter in patients with a pre-existing central venous catheter, the presence of entanglement should be assessed. In such cases, radiology-guided correction is recommended, as a blind attempt to disentangle can aggravate the condition.

According to previous studies on internal combustion engine applications using deep learning, deep learning model should be individually optimized and trained to predict different phenomena. This study introduces task transfer learning to predict transient nitrogen oxides (NO $_{\mathrm {x}}$ ), soot, and total hydrocarbon (THC) emissions, which are the major emissions from diesel engines. Using the concept of task transfer learning, when there is a pretrained model relevant to the target task, the model can be transferred to predict another phenomenon by training only the last two layers with hyperparameters of the pretrained model. This concept omits the need for optimizing and training separate models that can save computational time and cost. The results of task transfer learning were evaluated using Worldwide Harmonized Light Vehicles Test Procedure (WLTP) cycle data, which are representative transient cycles of the internal combustion engine, and all possible transfer cases with NOx, soot, and THC emissions were investigated. The R2 values of pretrained NOx, soot, and THC models were 0.9780, 0.9215, and 0.9390, respectively. The R2 gaps between the pretrained and transferred models were within 0.012, with a value of 0.0015 for the NOx emission, 0.011 for the soot emission, and 0.0115 for the THC emission. The relative mean absolute errors (MAEs) to the maximum emission values were approximately 0.57-0.82% for NOx emissions, 0.69-2.02% for soot emissions, and 1.52-2.42% for THC emissions. These accuracy results were comparable to the accuracy of the emission measurement device, which was better than that of the sensors for practical use in vehicles. The results indicated that task transfer learning was valid for predicting emissions of an internal combustion engine, and it achieved efficient organization of prediction models using a pretrained model.

IntroductionThere is insufficient evidence regarding the efficacy and safety of remimazolam in elderly patients. Therefore, this study evaluated the differences in the anesthesia characteristics and perioperative hemodynamic profiles of elderly patients receiving total intravenous anesthesia with remimazolam or propofol.MethodsEighty-four patients aged >65 years with an American Society of Anesthesiologists physical status of I–III were randomly assigned to Group R (receiving remimazolam, n = 42) or Group P (receiving propofol, n = 42). In Group R, remimazolam was initiated at a rate of 6 mg/kg/h until loss of consciousness (LOC) was achieved and maintained at 1 mg/kg/h subsequently. In Group P, 1.0–1.5 mg/kg of propofol was injected for 1 min and maintained at 100 μg/kg/min subsequently. The maintenance infusion rate was adjusted to maintain an appropriate depth of anesthesia until the end of the surgery. The primary outcome was the time to LOC. The depth of anesthesia scores and hemodynamic profiles were recorded perioperatively.ResultsThe time to LOC was significantly longer in Group R (120 s) than in Group P (60 s) (p

The fidelity of motor control requires the precise positional arrangement of motor pools and the establishment of synaptic connections between them. During neural development in the spinal cord, motor nerves project to specific target muscles and receive proprioceptive input from these muscles via the sensorimotor circuit. LIM-homeodomain transcription factors are known to play a crucial role in successively restricting specific motor neuronal fates. However, their exact contribution to limb-based motor pools and locomotor circuits has not been fully understood. To address this, we conducted an investigation into the role of Isl2, a LIM-homeodomain transcription factor, in motor pool organization. We found that deletion of Isl2 led to the dispersion of motor pools, primarily affecting the median motor column (MMC) and lateral motor column (LMC) populations. Additionally, hindlimb motor pools lacked Etv4 expression, and we observed reduced terminal axon branching and disorganized neuromuscular junctions in Isl2-deficient mice. Furthermore, we performed transcriptomic analysis on the spinal cords of Isl2-deficient mice and identified a variety of downregulated genes associated with motor neuron (MN) differentiation, axon development, and synapse organization in hindlimb motor pools. As a consequence of these disruptions, sensorimotor connectivity and hindlimb locomotion were impaired in Isl2-deficient mice. Taken together, our findings highlight the critical role of Isl2 in organizing motor pool position and sensorimotor circuits in hindlimb motor pools. This research provides valuable insights into the molecular mechanisms governing motor control and its potential implications for understanding motor-related disorders in humans.

Cancer-associated fibroblasts (CAFs) are a predominant and critical component of the tumour microenvironment. Here, the authors integrate and analyse single-cell RNA-seq data of CAFs across 10 common solid cancer types, identifying their plasticity and interactions with other cell types.

Abstract The detection of Epstein–Barr virus (EBV) in gastric cancer patients is crucial for clinical decision making, as it is related with specific treatment responses and prognoses. Despite its importance, the limited medical resources preclude universal EBV testing. Herein, we propose a deep learning-based EBV prediction method from H&E-stained whole-slide images (WSI). Our model was developed using 319 H&E stained WSI (26 EBV positive; TCGA dataset) from the Cancer Genome Atlas, and 108 WSI (8 EBV positive; ISH dataset) from an independent institution. Our deep learning model, EBVNet consists of two sequential components: a tumor classifier and an EBV classifier. We visualized the learned representation by the classifiers using UMAP. We externally validated the model using 60 additional WSI (7 being EBV positive; HGH dataset). We compared the model’s performance with those of four pathologists. EBVNet achieved an AUPRC of 0.65, whereas the four pathologists yielded a mean AUPRC of 0.41. Moreover, EBVNet achieved an negative predictive value, sensitivity, specificity, precision, and F1-score of 0.98, 0.86, 0.92, 0.60, and 0.71, respectively. Our proposed model is expected to contribute to prescreen patients for confirmatory testing, potentially to save test-related cost and labor.

Abstract The advent of single-cell RNA sequencing (scRNA-seq) has provided insight into the tumour immune microenvironment (TIME). This review focuses on the application of scRNA-seq in investigation of the TIME. Over time, scRNA-seq methods have evolved, and components of the TIME have been deciphered with high resolution. In this review, we first introduced the principle of scRNA-seq and compared different sequencing approaches. Novel cell types in the TIME, a continuous transitional state, and mutual intercommunication among TIME components present potential targets for prognosis prediction and treatment in cancer. Thus, we concluded novel cell clusters of cancer-associated fibroblasts (CAFs), T cells, tumour-associated macrophages (TAMs) and dendritic cells (DCs) discovered after the application of scRNA-seq in TIME. We also proposed the development of TAMs and exhausted T cells, as well as the possible targets to interrupt the process. In addition, the therapeutic interventions based on cellular interactions in TIME were also summarized. For decades, quantification of the TIME components has been adopted in clinical practice to predict patient survival and response to therapy and is expected to play an important role in the precise treatment of cancer. Summarizing the current findings, we believe that advances in technology and wide application of single-cell analysis can lead to the discovery of novel perspectives on cancer therapy, which can subsequently be implemented in the clinic. Finally, we propose some future directions in the field of TIME studies that can be aided by scRNA-seq technology.

Abstract Spatial transcriptomics is a newly emerging field that enables high‐throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems. By transitioning from conventional biological studies to “in situ” biology, spatial transcriptomics can provide transcriptome‐scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and discussions regarding future directions of spatial multi‐omics and their expanding roles in biomedical applications are emphasized.

Background: Evidence on whether long-term exposure to air pollution increases the mortality risk in patients with chronic obstructive pulmonary disease (COPD) is limited. Objectives: We aimed to investigate the associations of long-term exposure to particulate matter with diameter

Abstract We conducted a retrospective cohort study to evaluate the temporal pattern of incidence of chronic conditions after developing breast cancer using a population-based national registry. We selected 84,969 women with newly diagnosed breast cancer between 2002 and 2016 and a 1:10 sample of age-matched non-breast cancer controls (N = 1,057,674). The main study exposure was incident breast cancer, considered as a time-varying exposure. The outcomes were incident cases of leukemia, endometrial cancer, myeloma, cardiomyopathy, osteoporosis, end stage renal disease (ESRD), pulmonary fibrosis, hypothyroidism, type 2 diabetes, hypertension and hyperlipidemia. The development of breast cancer was associated with a significantly increased risk of all outcomes analyzed except for ESRD and hypertension. The fully-adjusted risks of leukemia (HR 3.09; 95% CI 2.11–4.51), cardiomyopathy (HR 2.65; 95% CI 1.90–3.68), endometrial cancer (HR 3.53; 95% CI 2.76–4.53), hypothyroidism (HR 1.29; 95% CI 1.19–1.40), pulmonary fibrosis (HR 1.84; 95% CI 1.12–3.02), and hyperlipidemia (HR 1.24; 95% CI 1.20–1.28) remained significantly elevated after more than 5 years since diagnosis. Optimal care for breast cancer survivors requires close collaboration between oncologists and allied health care professionals to identify and manage the long-term morbidity and mortality associated with these chronic conditions.

Objectives: To efficiently implement artificial intelligence (AI) software for medical applications, it is crucial to understand the acceptance, expected effects, expected performance, and concerns of software users. In this study, we examine the acceptance and expectation of the Dr. Answer AI software for prostate cancer. Methods: We conducted an online survey for urologists from August 13 to September 18, 2020. The target software is an AI-based clinical software called Dr. Answer AI software, used for prostate cancer diagnosis. We collected data from 86 urologists and conducted a basic statistical and multiple regression analysis using the R package. Results: The compatibility was significantly associated with the intention to use the Dr. Answer AI software. The expected average accuracy for the software ranges from 86.91% to 87.51%, and the urologists perceived that the cloud method is suitable to introduce the software. The most desirable function of the software for the specialists is predicting the occurrence of extracapsular extension, seminal vesicle invasion, and lymph node metastasis after radical prostatectomy. Finally, the primary concerns involved the cost, compatibility with existing systems, and obtaining accurate information from the software. Conclusions: Our results present an understanding of the acceptance, expected effects, expected performance, and concerns of software users. The results provide a guide to help AI software be properly developed and implemented in medical applications.

BackgroundDistributed research networks (DRNs) offer researchers the advantage of using various big data sets that are difficult to access and use. In addition, since the data are not physically exposed to the outside, it is possible to conduct research using medical data safely without data exposure. However, researchers still have difficulties and are concerned about using DRNs. Few studies involving DRNs have been conducted from the user’s viewpoint. Therefore, it is necessary to look at DRNs from the researcher’s point of view and find ways to facilitate the active use of DRNs. ObjectiveThis study aimed to identify the factors that made researchers hesitate to use DRNs and to derive a method to facilitate active DRN use. MethodsWe conducted a web-based survey of people working in the medical fields, such as hospitals and universities. We used 131 respondents’ data from a survey from December 6 to 17, 2021. We conducted multiple regression analyses to determine the factors affecting the postponement of using DRNs. In addition, 2 independent sample t tests were conducted to analyze the difference between the 2 groups according to the following factors: organization, gender, experience with DRNs, length of the research career, position, and age. ResultsPerformance risk (t5=2.725, P=.007) and workload from DRNs (t5=3.543, P=.001) were significantly associated with users’ postponement of DRN use. Researchers working at hospitals were found to feel more burdened by DRN use than researchers working at universities (t129=1.975, P=.05). It was also found that women perceived a higher privacy risk of DRNs than men (t129=–2.303, P=.02) and that those who had experience using DRNs delayed their use less than those without experience (t129=–4.215, P

Objectives: Big data-based multicenter medical research is expected to bring significant advances to cancer treatment worldwide. However, there are concerns related to data sharing among multicenter networks. Clinical data can be shielded by firewalls using distributed research networks (DRNs). We attempted to develop DRNs for multicenter research that can be easily installed and used by any institution. Patients and Methods: We propose a DRN for multicenter cancer research called the cancer research line (CAREL) and present a data catalog based on a common data model (CDM). CAREL was validated using 1723 patients with prostate cancer and 14 990 patients with lung cancer in a retrospective study. We used the attribute-value pairs and array data type JavaScript object notation (JSON) format to interface third-party security solutions such as blockchain. Results: We developed visualized data catalogs of prostate and lung cancer based on the observational medical outcomes partnership (OMOP) CDM, from which researchers can easily browse and select relevant data. We made the CAREL source code readily available for download and application for relevant purposes. In addition, it is possible to realize a multicenter research network using CAREL development sources. Conclusion: CAREL source can enable medical institutions to participate in multicenter cancer research. Our technology is open source, so small institutions that cannot afford to spend high costs can use it to develop a platform for multicenter research.

Background: Metabolic syndrome characterized by abdominal obesity, high blood pressure, elevated fasting glucose and triglyceride levels and low high-density lipoprotein cholesterol level is associated with pro-inflammatory state, increased risk for atherosclerosis, and multiple cancers. Our previous results on subjects with metabolic syndrome showed that 4-week dawn-to-dusk (sunset) dry fasting resulted in significant changes in the serum proteome and improvement in several metabolic risk factors. Peripheral blood mononuclear cells (PBMC) proteomics is a powerful tool that can provide mechanistic insights into how dawn-to-dusk dry fasting affects protein expression in metabolic pathways at cellular level. In this study, we determined whether dawn-to-dusk dry fasting would induce favorable changes in PBMC proteome in subjects with metabolic syndrome, similar to the changes induced by dawn-to-dusk dry fasting in the same subjects' serum proteome. Methods: We conducted a prospective study on subjects with metabolic syndrome and collected blood specimens before 4-week dawn-to-dusk dry fasting, at the end of 4-week dawn-to-dusk dry fasting, and one week after 4-week dawn-to-dusk dry fasting. We performed untargeted proteomics using nano ultra-high performance liquid chromatography-tandem mass spectrometry to assess the impact of 4-week dawn-to-dusk dry fasting on PBMC proteome. Results: There were 14 subjects with metabolic syndrome with a mean age of 59 who fasted from dawn to dusk (strict dry fasting without any liquid or food intake) for more than 14 h daily for 29 days. The quantitative proteome analysis showed that apolipoprotein B (APOB) gene protein products (GP) levels were downregulated and had the most statistical significance of the observed difference at the end of 4-week dawn-to-dusk dry fasting (P = 0.008) and one week after 4-week dawn-to-dusk dry fasting (P = 0.0004) compared with the levels before 4-week dawn-to-dusk dry fasting. The comparison between GP levels before and at the end of 4-week dawn-to-dusk dry fasting showed an alteration in the expression of genes associated with lipid and atherosclerosis pathway (P = 6.014e-4) and C-type lectin receptor signaling pathway (P = 1.064e-5). The genes that were differentially expressed in the lipid and atherosclerosis pathway were APOB (P = 0.008), CD36 (P = 0.040), CALM1, CALM2, CALM3 (P = 0.015), and HSPA8 (P = 0.047). One of the differentially expressed genes in the C-type lectin receptor signaling pathway was lymphocyte-specific protein 1 (LSP1), which showed an average of 19-fold increase at the end of 4-week dawn-to-dusk dry fasting compared with the GP levels before fasting (P = 0.004). Several GPs associated with tumor-suppressor effect (TUBB4B, LSP1, ACTR3B) were upregulated, and GPs associated with tumor-promoter effect (CD36, CALM1, CALM2, CALM3, FLOT2, PPIF) were downregulated at the end of 4-week dawn-to-dusk dry fasting or one week after 4-week dawn-to-dusk dry fasting compared with the GP levels before 4-week dawn-to-dusk dry fasting. Conclusion: Based on our results, we conclude that in subjects with metabolic syndrome, 4-week dawn-to-dusk dry fasting induced anti-atherosclerotic, anti-inflammatory, and anti-tumorigenic PMBC proteome. Randomized, controlled clinical trials are needed to further investigate the effect of dawn-to-dusk dry fasting on subjects with chronic metabolic diseases and metabolic syndrome-induced cancers.

Background: Alternatives to the Friedewald low-density lipoprotein cholesterol (LDL-C) equation have been proposed. Objective: To compare the accuracy of available LDL-C equations with ultracentrifugation measurement. Methods: We used the second harvest of the Very Large Database of Lipids (VLDbL), which is a population-representative convenience sample of adult and pediatric patients (N = 5,051,467) with clinical lipid measurements obtained via the vertical auto profile (VAP) ultracentrifugation method between October 1, 2015 and June 30, 2019. We performed a systematic literature review to identify available LDL-C equations and compared their accuracy according to guideline-based classification. We also compared the equations by their median error versus ultracentrifugation. We evaluated LDL-C equations overall and stratified by age, sex, fasting status, and triglyceride levels, as well as in patients with atherosclerotic cardiovascular disease, hypertension, diabetes, kidney disease, inflammation, and thyroid dysfunction. Results: Analyzing 23 identified LDL-C equations in 5,051,467 patients (mean±SD age, 56±16 years; 53.3% women), the Martin/Hopkins equation most accurately classified LDL-C to the correct category (89.6%), followed by the Sampson (86.3%), Chen (84.4%), Puavilai (84.1%), Delong (83.3%), and Friedewald (83.2%) equations. The other 17 equations were less accurate than Friedewald, with accuracy as low as 35.1%. The median error of equations ranged from –10.8 to 18.7 mg/dL, and was best optimized using the Martin/Hopkins equation (0.3, IQR–1.6 to 2.4 mg/dL). The Martin/Hopkins equation had the highest accuracy after stratifying by age, sex, fasting status, triglyceride levels, and clinical subgroups. In addition, one in five patients who had Friedewald LDL-C 70 mg/dL by the Martin/Hopkins equation. Conclusions: Most proposed alternatives to the Friedewald equation worsen LDL-C accuracy, and their use could introduce unintended disparities in clinical care. The Martin/Hopkins equation demonstrated the highest LDL-C accuracy overall and across subgroups.

Due to the complex structure and function of the kidneys, the mechanism of kidney disease is unclear. In particular, transcriptomics approaches at the bulk level are unable to differentiate primary autonomous responses, which lead to disease development, from secondary cell non-autonomous responses. Single-cell analysis techniques can overcome the limitations inherent in the measurement of heterogeneous cell populations and clarify the central issues in kidney biology and disease pathogenesis. Single-cell sequencing helps in identifying disease-related biomarkers and pathways, stratifying patients, and deciding on appropriate treatment methods. Here we review a variety of single-cell analysis techniques and single-cell transcriptomics studies performed in the field of nephrology. Moreover, we discuss the future prospects of single-cell analysis-based precision medicine in nephrology.

Purpose: Prostate cancer has a low mortality rate and requires persistent treatment; however, treatment decisions are challenging. Because prostate cancer is complex, the outcomes warrant thorough follow-up evaluation for appropriate treatment. Electronic health records (EHRs) do not present intuitive information. This study aimed to develop a Clinical Decision Support System (CDSS) for prognosis management of radical prostatectomy. Methods: We used data from 5,199 prostate cancer patients from three hospitals’ EHRs in South Korea, comprising laboratory results, surgery, medication, and radiation therapy. We used open source R for data preprocessing and development of web-based visualization system. We also used R for automatic calculation functionalities of two factors to visualize the data, e.g., Prostate-Specific Antigen Doubling Time (PSADT), and four Biochemical Recurrence (BCR) definitions: American Society of Therapeutic Radiology and Oncology (ASTRO), Phoenix, consecutive PSA > 0.2 ng/mL, and PSA > 0.2 ng/mL. Results: We developed the Prostate Cancer Trajectory Map (PCT-Map) as a CDSS for intuitive visualization of serial data of PSA, testosterone, surgery, medication, radiation therapy, BCR, and PSADT. Conclusions: The PCT-Map comprises functionalities for BCR and PSADT and calculates and visualizes the newly added patient data automatically in a PCT-Map data format, thus optimizing the visualization of patient data and allowing clinicians to promptly access patient data to decide the appropriate treatment.

Haustoria of parasitic plants have evolved sophisticated traits to successfully infect host plants. The degradation and modification of host cell walls enable the haustorium to effectively invade host tissues. This study focused on two APETALA2/ETHYLENE RESPONSE FACTOR (ERF) genes and a set of the cell wall enzyme genes principally expressed during the haustorial invasion of Cuscuta campestris Yuncker. The orthogroups of the TF and cell wall enzyme genes have been implicated in the cell wall degradation and modification activities in the abscission of tomatoes, which are currently the phylogenetically closest non-parasitic model species of Cuscuta species. Although haustoria are generally thought to originate from root tissues, our results suggest that haustoria have further optimized invasion potential by recruiting regulatory modules from other biological processes.

This paper proposes a resolver offset calibration algorithm that can detect offset with high precision just by configuring a very simple experimental environment. This use of a simple experimental environment holds a great advantage for large-capacity permanent magnet synchronous motors (PMSMs) that require substantial resources to configure an experimental environment, such as EV traction motors. The proposed algorithm is designed based on the PMSM voltage equation. The algorithm is first verified for its validity through simulation with Simulink. Subsequently, it is implemented based on the C-language and installed on an inverter for verification experiments. The implemented algorithm is very simple and requires little execution time and memory. The error in the calibrated offset is experimentally found to converge within 3-bits based on 12-bits (the resolution of the commonly used RDC), and the results have very little deviation.

To detect the three-phase current in the complex plane of a DC link shunt inverter, an algorithm for restoring the current is required. In this paper, a method of dividing the detection voltage and the compensation voltage to match the output voltage as much as possible to reduce the total harmonic distortion while restoring the current is proposed. In addition, an overmodulation algorithm for a 12-step output, which corresponds to the largest voltage in a DC link shunt inverter, is proposed, and a current recovery method in the overmodulation region is proposed. To determine how to ensure a linear output voltage, the fundamental frequency of the output voltage is analyzed through a Fourier series, and a new voltage vector whose fundamental frequency amplitude is equal to the amplitude of the command voltage is calculated. Finally, the performance of the proposed algorithm is verified through simulation and experimentation. The output of a motor was increased by using overmodulation, and the harmonics of the current based on the output voltage were analyzed through a Fourier series.

Digital pathology (DP) is no longer an unfamiliar term for pathologists, but it is still difficult for many pathologists to understand the engineering and mathematics concepts involved in DP. Computer-aided pathology (CAP) aids pathologists in diagnosis. However, some consider CAP a threat to the existence of pathologists and are skeptical of its clinical utility. Implementation of DP is very burdensome for pathologists because technical factors, impact on workflow, and information technology infrastructure must be considered. In this paper, various terms related to DP and computer-aided pathologic diagnosis are defined, current applications of DP are discussed, and various issues related to implementation of DP are outlined. The development of computer-aided pathologic diagnostic tools and their limitations are also discussed.

Background Accurate measurement of the cholesterol within lipoprotein(a) (Lp[a]‐C) and its contribution to low‐density lipoprotein cholesterol (LDL‐C) has important implications for risk assessment, diagnosis, and treatment of atherosclerotic cardiovascular disease, as well as in familial hypercholesterolemia. A method for estimating Lp(a)‐C from particle number using fixed conversion factors has been proposed (Lp[a]‐C from particle number divided by 2.4 for Lp(a) mass, multiplied by 30% for Lp[a]‐C). The accuracy of this method, which theoretically can isolate “Lp(a)‐free LDL‐C,” has not been validated. Methods and Results In 177 875 patients from the VLDbL (Very Large Database of Lipids), we compared estimated Lp(a)‐C and Lp(a)‐free LDL‐C with measured values and quantified absolute and percent error. We compared findings with an analogous data set from the Mayo Clinic Laboratory. Error in estimated Lp(a)‐C and Lp(a)‐free LDL‐C increased with higher Lp(a)‐C values. Median error for estimated Lp(a)‐C

Objective: Major guidelines recommend the use of secondary targets, such as non-HDL-C and apoB, to further reduce cardiovascular risk. We aimed to evaluate the proportion at which newer, more aggressive secondary lipid targets are exceeded in patients with LDL-C

Persistently low levels of estimated glomerular filtration rate (eGFR) are a biomarker of chronic kidney disease. Here, the authors reinterpret the genetic architecture of kidney function across ancestries, to identify not only genes, but the tissue and anatomical contexts of renal homeostasis.

Patients with diabetes commonly develop diabetic kidney disease (DKD). Here Gluck et al. identify a set of probes differentially methylated in renal samples from patients with DKD, and find that inclusion of these methylation probes improves current prediction models of renal function decline.

This paper comparatively analyzes the static random-access memory (SRAM) cell designs for fault tolerance. Since SRAM cells are sensitive to radiation-induced single event upsets, various circuit-level approaches have been applied. Compared to the conventional SRAM cell circuits, one possibility is adding redundant storage nodes by means of additional transistors. The strength and weakness of the SRAM cells in terms of various performance aspects—speed, area, power, stability, fault tolerance, etc.—according to the design approaches are compared analytically and discussed. The discussion concludes that, in the future, it is paramount to develop an SRAM cell design with a mitigated trade-off between read/write performance and SEU tolerance.

BackgroundSince the COVID-19 outbreak, South Korea has been engaged in various efforts to overcome the pandemic. One of them is to provide app-based COVID-19–related services to the public. As the pandemic continues, a need for various apps has emerged, including COVID-19 apps that can support activities aimed at overcoming the COVID-19 pandemic. ObjectiveWe aimed to determine which apps were considered the most necessary according to users and evaluate the current status of the development of COVID-19–related apps in South Korea. We also aimed to determine users’ acceptance and concerns related to using apps to support activities to combat COVID-19. MethodsWe collected data from 1148 users from a web-based survey conducted between November 11 and December 6, 2020. Basic statistical analysis, multiple response analysis, and the Wilcoxon rank sum test were performed using R software. We then manually classified the current status of the development of COVID-19–related apps. ResultsIn total, 68.4% (785/1148) of the respondents showed high willingness to protect themselves from COVID-19 by using related apps. Users considered the epidemiological investigation app to be the most necessary app (709/1148, 61.8%) overall, followed by the self-management app for self-isolation (613/1148, 53.4%), self-route management app (605/1148, 52.7%), COVID-19 symptom management app (483/1148, 42.1%), COVID-19–related information provision app (339/1148, 29.5%), and mental health management app (270/1148, 23.5%). Despite the high intention to use these apps, users were also concerned about privacy issues and media exposure. Those who had an underlying disease and had experience using COVID-19–related apps showed significantly higher intentions to use those apps (P=.05 and P=.01, respectively). ConclusionsTargeting users is very important in order to design and develop the most necessary apps. Furthermore, to gain the public’s trust and make the apps available to as many people as possible, it is vital to develop diverse apps in which privacy protection is maximized.

Xanthomonas oryzae pv. oryzae (Xoo) is a plant pathogen responsible for causing bacterial blight in rice. The immediate alterations in Xoo upon initial contact with rice are essential for pathogenesis. We studied time-resolved genome-wide gene expression in pathogenicity-activated Xoo cells at the transcriptome and proteome levels. The early response genes of Xoo include genes related to cell motility, inorganic ion transport, and effectors. The alteration of gene expression is initiated as early as few minutes after the initial interaction and changes with time. The time-resolved comparison of the transcriptome and proteome shows the differences between transcriptional and translational expression peaks in many genes, although the overall expression pattern of mRNAs and proteins is conserved. The discrepancy suggests an important role of translational regulation in Xoo at the early stages of pathogenesis. The gene expression analysis using time-resolved transcriptome and proteome provides unprecedented valuable information regarding Xoo pathogenesis.

Objectives: To develop a model to predict biochemical recurrence (BCR) after radical prostatectomy (RP), using artificial intelligence (AI) techniques. Patients and Methods: This study collected data from 7,128 patients with prostate cancer (PCa) who received RP at 3 tertiary hospitals. After preprocessing, we used the data of 6,755 cases to generate the BCR prediction model. There were 16 input variables with BCR as the outcome variable. We used a random forest to develop the model. Several sampling techniques were used to address class imbalances. Results: We achieved good performance using a random forest with synthetic minority oversampling technique (SMOTE) using Tomek links, edited nearest neighbors (ENN), and random oversampling: accuracy = 96.59%, recall = 95.49%, precision = 97.66%, F1 score = 96.59%, and ROC AUC = 98.83%. Conclusion: We developed a BCR prediction model for RP. The Dr. Answer AI project, which was developed based on our BCR prediction model, helps physicians and patients to make treatment decisions in the clinical follow-up process as a clinical decision support system.

Background Studies have shown that short‐term exposure to air pollution is associated with cardiac arrhythmia hospitalization and mortality. However, the relationship between long‐term particulate matter air pollution and arrhythmias is still unclear. We evaluate the prospective association between particulate matter (PM) air pollution and the risk of incident arrhythmia and its subtypes. Methods and Results Participants were drawn from a prospective cohort study of 178 780 men and women who attended regular health screening exams in Seoul and Suwon, South Korea, from 2002 to 2016. Exposure to PM with an aerodynamic diameter of ≤10 and ≤2.5 μm (PM10 and PM2.5, respectively) was estimated using a land‐use regression model. The associations between long‐term PM air pollution and arrhythmia were examined using pooled logistic regression models with time‐varying exposure and covariables. In the fully adjusted model, the odds ratios (ORs) for any arrhythmia associated with a 10 μg/m3 increase in 12‐, 36‐, and 60‐month PM10 exposure were 1.15 (1.09, 1.21), 1.12 (1.06, 1.18), and 1.14 (1.08, 1.20), respectively. The ORs with a 10 μg/m3 increase in 12‐ and 36‐month PM2.5 exposure were 1.27 (1.15, 1.40) and 1.10 (0.99, 1.23). PM10 was associated with increased risk of incident bradycardia and premature atrial contraction. PM2.5 was associated with increased risk of incident bradycardia and right bundle‐branch block. Conclusions In this large cohort study, long‐term exposure to outdoor PM air pollution was associated with increased risk of arrhythmia. Our findings indicate that PM air pollution may be a contributor to cardiac arrhythmia in the general population.

Smartphone usage characteristics are useful for identification of the risk factors for smartphone addiction. Risk rating scores can be developed based on smartphone usage characteristics. This study aimed to investigate the smartphone addiction risk rating (SARR) score using smartphone usage characteristics. We evaluated 593 smartphone users using online surveys conducted between January 2 and January 31, 2019. We identified 102 smartphone users who were addicted to smartphones and 491 normal users based on the Korean Smartphone Addiction Proneness Scale for Adults. A multivariate logistic regression analysis was used to identify significant risk factors for smartphone addiction. The SARR score was calculated using a nomogram based on the significant risk factors. Weekend average usage time, habitual smartphone behavior, addictive smartphone behavior, social usage, and process usage were the significant risk factors associated with smartphone addiction. Furthermore, we developed the SARR score based on these factors. The SARR score ranged between 0 and 221 points, with the cut-off being 116.5 points. We developed a smartphone addiction management application using the SARR score. The SARR score provided insights for the development of monitoring, prevention, and prompt intervention services for smartphone addiction.

Bulk tissue RNA-seq data reveals transcriptomic profiles but masks the contributions of different cell types. Here, the authors develop a new method for estimating cell type proportions from bulk tissue RNA-seq data guided by multi-subject single-cell expression reference.

Bone marrow mesenchymal lineage cells are a heterogeneous cell population involved in bone homeostasis and diseases such as osteoporosis. While it is long postulated that they originate from mesenchymal stem cells, the true identity of progenitors and their in vivo bifurcated differentiation routes into osteoblasts and adipocytes remain poorly understood. Here, by employing large scale single cell transcriptome analysis, we computationally defined mesenchymal progenitors at different stages and delineated their bi-lineage differentiation paths in young, adult and aging mice. One identified subpopulation is a unique cell type that expresses adipocyte markers but contains no lipid droplets. As non-proliferative precursors for adipocytes, they exist abundantly as pericytes and stromal cells that form a ubiquitous 3D network inside the marrow cavity. Functionally they play critical roles in maintaining marrow vasculature and suppressing bone formation. Therefore, we name them marrow adipogenic lineage precursors (MALPs) and conclude that they are a newly identified component of marrow adipose tissue.

One of the main challenges in the adoption of artificial intelligence-based tools, such as integrated decision support systems, is the complexities of their application. This study aimed to define the relevant parameters that can be used as indicators for real-time detection of heat stress and subclinical mastitis in dairy cows. Moreover, this study aimed to demonstrate the use of a developed data-mining hub as an artificial intelligence-based tool that integrates the defined relevant information (parameters or traits) in accurately identifying the condition of the cow. A comprehensive theoretical framework of the data-mining hub is demonstrated, the selection of the parameters that were used for the data-mining hub is listed, and the relevance of the traits is discussed. The practical application of the data-mining hub has shown that using 21 parameters instead of 13 and 8 parameters resulted in a high overall accuracy of detecting heat stress and subclinical mastitis in dairy cows with a high precision effect reflecting a low percentage of misclassifying the conditions of the dairy cows. This study has developed an innovative approach in which combined information from different independent data was used to accurately detect the health and wellness status of the dairy cows. It can also be implied that an artificial intelligence-based tool such as the proposed theoretical data-mining hub of dairy cows could maximize the use of continuously generated and underutilized data in farms, thus ultimately simplifying repetitive and difficult decision-making tasks in dairy farming.

The use of coupled-bias common-mode feedback allows a low-voltage differential signaling driver, implemented in a deep-submicron process, to adjust its output common-mode voltage, as well as the output swing and equalization strength independently. This form of differential signaling driver has been incorporated in a 16-channel transmitter for the timing controller of an intra-panel interface. The flexibility of its output characteristics allows it to accommodate the range of channel characteristics commonly found in the intra-panel interface of an ultra-high-definition display. Fabricated in a 28-nm bulk CMOS process, our 16-channel transmitter occupies 1.39 mm2. Its total power consumption is 203.2 mW with a supply voltage of 1.05 V, running at 3.2 Gb/s with the maximum output swing. Measurements confirm that the transmitter can operate from 0.8 to 3.2 Gb/s with channels that replicate those encountered in an intra-panel interface. The maximum controllable range of the output swing, the output common-mode voltage, and equalization are 216-1072 mVdiff, 291-604 mV, and 0-14.6 dB, respectively.

Chronic kidney disease (CKD) has diverse phenotypic manifestations including structural (such as fibrosis) and functional (such as glomerular filtration rate and albuminuria) alterations. Gene expression profiling has recently gained popularity as an important new tool for precision medicine approaches. Here we used unbiased and directed approaches to understand how gene expression captures different CKD manifestations in patients with diabetic and hypertensive CKD. Transcriptome data from ninety-five microdissected human kidney samples with a range of demographics, functional and structural changes were used for the primary analysis. Data obtained from 41 samples were available for validation. Using the unbiased Weighted Gene Co-Expression Network Analysis (WGCNA) we identified 16 co-expressed gene modules. We found that modules that strongly correlated with eGFR primarily encoded genes with metabolic functions. Gene groups that mainly encoded T-cell receptor and collagen pathways, showed the strongest correlation with fibrosis level, suggesting that these two phenotypic manifestations might have different underlying mechanisms. Linear regression models were then used to identify genes whose expression showed significant correlation with either structural (fibrosis) or functional (eGFR) manifestation and mostly corroborated the WGCNA findings. We concluded that gene expression is a very sensitive sensor of fibrosis, as the expression of 1654 genes correlated with fibrosis even after adjusting to eGFR and other clinical parameters. The association between GFR and gene expression was mostly mediated by fibrosis. In conclusion, our transcriptome-based CKD trait dissection analysis suggests that the association between gene expression and renal function is mediated by structural changes and that there may be differences in pathways that lead to decline in kidney function and the development of fibrosis, respectively.